Every degree program is a connected system of courses, prerequisites, and timing. Curricular analytics maps that system and measures where its structure makes graduation harder than it needs to be — a shared, data-informed starting point for the curriculum work faculty already do.
What It Is
Curricular analytics treats every degree program as what it actually is: a connected system of courses, prerequisites, and timing. Instead of reviewing a program course by course, it looks at the whole structure at once, where it's fragile, where it's overbuilt, and where a small change would help the most students.
At Texas State, curricular analytics is the practice of mapping a program's official course sequence into a network graph, then measuring that graph the way an engineer would stress-test a structure. The map comes from data programs already report: the catalog, the prerequisite and corequisite chain, and course-level pass/fail history.
The approach builds on a framework originally developed by a team of engineering researchers who argued that a student's path through a degree, not any single course, is the most foundational factor in whether they finish. That framework splits a curriculum's difficulty into two parts: structural complexity, how courses are ordered and connected, which is what this analysis measures, and instructional complexity, the quality of teaching and support within a course, which stays with program faculty and student success partners.
In one sentence: It doesn't ask “is this a good program?” It asks “where does this plan's architecture make it harder than it needs to be for a student to reach graduation?”
What It Works With, and What It Detects
The analysis runs on data every program already has. What it hands back is a diagnosis a committee can act on.
| What It Works With | What It Detects |
|---|---|
| The official course catalog — every required and elective course in the current degree plan. | Blocking factor — courses that hold up the largest number of downstream courses if a student fails or delays them. |
| Prerequisite and corequisite chains — the exact dependency structure between courses, as published. | Delay factor — courses whose failure adds the most time to a student's path to graduation. |
| Term-by-term degree plans — the recommended sequence a student is expected to follow. | Centrality — courses sitting at the busiest crossroads of the curriculum, where problems ripple furthest. |
| Credit-hour totals — how a program's length compares to the 120-credit-hour baseline. | Complexity score — a single, comparable number that sums a program's total blocking factor and total delay factor across every required course. |
| Credit overage — programs sitting meaningfully above 120 credit hours without a clear accreditation driver. |
What It Makes Possible
Once a program's structure is mapped, the same graph supports a range of work beyond a single review cycle.
— Identifying bottlenecks and key courses — Pinpoints the specific courses that hold up the most students or carry the most downstream risk if a student struggles in them.
— Data-informed curriculum reform — Gives curriculum committees a structural basis for revision decisions, rather than starting reform from anecdote alone.
— Program review & peer comparisons — Lets a program's complexity be benchmarked against its own history and against comparable programs at peer institutions.
— Personalized degree plans — Supports building degree plans optimized for an individual student's starting point, pace, and course history.
— 2-to-4-year articulation pathways — Help design and validate transfer pathways from two-year institutions so that credits map cleanly onto a four-year sequence.
What It Prevents
Left unmeasured, structural complexity doesn't stay neutral, it quietly costs students time, money, credits, and in some cases, whether they finish at all.
— Invisible bottlenecks — that persist catalog cycle after catalog cycle because no measure ever surfaced them for review.
— Compounding delays — where a single gateway course failure pushes a student's graduation back a full year instead of a term.
— Uneven impact on transfer and working students — who have the least flexibility to absorb an unplanned extra semester.
— Degree plans that outgrow 120 credit hours — by accumulation, without a deliberate decision that the extra credit is worth it.
— Curriculum changes made in isolation — a single course edit that shifts prerequisites without anyone seeing the downstream effect on the rest of the plan.
How It Supports Program Development and Revitalization
Complexity scores aren't a verdict, they're a starting point for the same work faculty already do in curriculum committees, just with a clearer picture of where to focus.
— Evidence for sequencing decisions — Gives program faculty a concrete basis for reordering, splitting, or redesigning a gateway course, instead of relying on anecdote about where students get stuck.
— A shared starting point for program review — Feeds directly into scheduled program review and accreditation self-studies, so complexity data is already on the table rather than assembled from scratch.
— A framework for revitalizing aging programs — Helps identify which long-standing programs have accumulated structural complexity over time and gives revitalization efforts a concrete, measurable target.
— A way to build lean from the start — Lets programs test a proposed sequence for bottlenecks and credit-hour load before it's finalized, rather than discovering issues after students are already enrolled.
How We're Putting It to Work at Texas State
Curricular analytics isn't a separate process bolted onto the catalog cycle, it's built into the curriculum cycle programs already follow, from September through May.
| Timing | What happens |
|---|---|
| September | Every undergraduate program is scored for the current catalog; high-complexity programs are identified and notified. |
| Fall term | Program faculty and staff work with their college to review bottleneck data and draft proposed changes. The college reviews and signs off on proposed changes before moving them to the committee. |
| Spring term | Changes move through normal curriculum committee governance, unchanged by this process — only better informed by it. |
| May | Results are documented and carried into the next catalog cycle, so progress is tracked year over year. |
New addition: Now built into new program proposals
New undergraduate program proposals no longer wait until after launch to be graphed. A curricular analytics review is now a required part of the proposal package, so a program's structure is stress-tested before it goes to the college, the university committee, and THECB — while it's still just a plan on paper and easiest to adjust. The proposed degree plan is mapped into a course network, blocking and delay factors, and the complexity score are estimated, credit-hour totals are checked against the 120-hour baseline, and findings travel with the proposal through college and university approval alongside standard THECB, SACSCOC, and specialized-accreditor review.
Who to Contact
The Office of Curriculum and Academic Programs coordinates Curricular Analytics at Texas State University.
Office of Curriculum and Academic Programs
Reach out to have your program's degree plan mapped and scored, ask questions about where your program stands, or get help preparing the curricular analytics review for a new program proposal.
For questions and inquiries, email curriculum@txstate.edu or submit a ticket through the Provost’s Office Service Center.
The Office of Curriculum and Academic Programs coordinates Curricular Analytics at Texas State University. Access their resources on Curricular Analytics by clicking here.